(auteur) Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management.

(Auteur) Typographic variables were only briefly discussed by Bertin with no example applications. We review historic examples to extend Bertin’s framework with literal encoding, 10 typographic attributes, variations on scope including characters, words, phrases and paragraphs, and more layout types. We then apply the framework to Bertin’s population dataset to create 11 new typographic visualizations. The approach raises questions for new research such as readability, semantic association, interaction, and comprehension.

(auteur) Key message : Post-stratification based on remotely sensed data is an efficient method in estimating regional-level results in the operational National Forest Inventory. It also enables calculating the results accurately for smaller areas than with the default method of using the field plots only.
Context : The utilization of auxiliary information in survey sampling through model-assisted estimation or post-stratification has gained popularity in forest inventory recently. However, post-stratification at a large scale involves practical concerns such as the availability of auxiliary data independent of the sample at hand, and a large number of variables for which the results are needed.
Aims : We assessed the efficiency of two different types of post-stratification, either post-stratifying for each variable of interest separately or using one post-stratification for all variables, compared to the estimation based on the field sample plots only. In addition, we examined the precision of area and volume estimates, and the efficiency of post-stratification at different spatial scales.
Methods : For post-stratification, we used the volume maps based on Landsat satellite imagery, digital map data, and the sample plot data of the previous inventory. The efficiencies of post-stratifications based on the mean volume and the mean volumes by tree species were compared.
Results : In estimating the total volume, the relative efficiency of post-stratification compared to field plot based estimation was 1.54–3.54 over the provinces in South Finland. In estimating the volumes by tree species groups, the relative efficiency was 0.93–2.39. The gain with a separate stratification compared to the stratification based on total mean volume for all variables was at largest 0.69. In the small test areas, the relative standard errors of the total volume estimates decreased on average by 33% by using post-stratification instead of sample plots only. The mean relative efficiency was 2.36.
Conclusion : The utilization of an old forest resources map and post-stratification based on the mean volume is an operational approach for the National Forest Inventory. Post-stratification also enables calculating the results accurately for markedly smaller areas than with the field plots only. Post-stratification reduced the probability of very high sampling variances, making the results more robust.